What are degrees of freedom? How are the calculated?
Degrees of freedom (df) are determined by sample size and are the number of scores free to vary once the researcher calculates the known means (Cozby & Bates, 2012; Jackson, 2012). Degrees of freedom are calculated by subtracting the total number of groups from the total number of participants (Cozby & Bates, 2012), e.g. df = N – 1 for one group, and df = n1 + n2 – 2 when comparing two means. There are reasons DF is appropriate for small sample sizes and why as sample sizes increase DF becomes less important. At some point, the advantage of DF goes away the same result will almost occur with or without DF.
3. What do inferential statistics allow you to infer?
Inferential statistics allows the researcher to draw conclusions about a population based on data collected from a sample of that specific population (Jackson, 2012).
4. What is the General Linear Model (GLM)? Why does it matter?
The GLM is a “system of equations that is used as the mathematical framework for most of the statistical analyses used in applied social research” (Trochim & Donnelly, 2008, p. 297), and is the “foundation for the t-test, ANOVA, ANCOVA, regression analysis, and many of the multivariate methods” (Ibid.). The reason GLM matters is because of its wide application due to its generality. Some research outcomes can be summarized with the GLM system of equations (Trochim & Donnelly, 2008).
5. Compare and contrast parametric and nonparametric statistics. Why and in what types of cases would you use one over the other?
Parametric statistics are tests based on a normal distribution (Field, 2013). Field (2013) posits four basic assum...
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...to sampling error alone, the researcher rejects the null hypothesis. Yatani (2009), proffers that NHST is a “statistical method for testing whether the factor we are talking about has the effect on our observation” (para. 1). The assumption of the model is based on the assumption that a statistically significant result rejects the null hypothesis.
NHST has to do with establishing an effect size or other measurement parameter. The term "null" was established by R. Fisher in his work establishing a statistical strategy of formulating a proposal that research data be able to be rejected. The most widely used research technique for testing null hypotheses is by population effect sizes. The test looks at the correlation coefficient or a difference between means. When the coefficient (ES) is zero, there is no relationship. This means there is no effect (Cohen, 1994).
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